不用手动转录音频，自动从语音里挖出阿尔茨海默病早期信号，还能解释推理过程，比黑盒模型更让人放心。
该研究提出一种自动化流程，直接从语言流畅性测试的音频中提取阿尔茨海默病进展指标。方法使用预训练基础模型处理原始音频，构建贝叶斯网络进行推理，并解释各语言标记间的定性关系。系统成功恢复了已知的临床知识，还发现了新的语言学标记关联。相关论文已发布在arXiv上。
A Neurosymbolic Approach for Explainable Early Diagnosis of Alzheimer's Disease
Identifying reliable Alzheimer's disease (AD) markers typically requires manual, labor-intensive transcription and expert analysis, limiting its scale. We introduce an automated pipeline that extracts qualitative knowledge about potential AD progression indicators directly from audio recordings of verbal fluency tests. Our method uses pretrained foundation models to process raw audio and extract clinically relevant variables to construct a Bayesian Network (BN); this BN is used to reason about the AD progression markers and infer their qualitative relationships. Our system successfully recovers known clinical knowledge and identifies novel relationships between linguistic markers.